Towards a Smart Privacy-Preserving Incentive Mechanism for Vehicular Crowd Sensing

Abstract

Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.